This section is intended to introduce various aspects of the art, which may be associated with portions of the disclosed techniques and methodologies. This discussion is believed to assist in providing a framework to facilitate a better understanding of particular aspects of the disclosed techniques. Accordingly, it should be understood that this section should be read in this light, and not necessarily as admissions of prior art.
In recent years, there has been considerable interest in devising evolutionary geologic models which focus on the underlying physical processes and attempt to resolve them at pertinent spatial and temporal scales. One example of such a model is described in U.S. Patent Publication No. 2007/0219725, entitled “A Method For Evaluating Sedimentary Basin Properties By Numerical Modeling Of Sedimentation Processes”, by Sun, et al., filed on Aug. 23, 2005. As this approach, commonly referred to as “process-” or “physics-”based geologic modeling, relies solely on fundamental laws of physics in its time evolution, it has the clear advantage of curtailing the inclusion of non-physical ad-hoc parameters which plagues most statistics-based geologic models. Although process-based geologic modeling is considered to be a great improvement over purely statistical techniques, its business relevance and value can only be realized when the modeler can also judiciously choose its parameters such that the model prediction corresponds closely to the available field data. Integration of field and production data into physics-based models is known as “conditioning” in geology.
One approach to conditioning of physics-based geologic models is to pose the problem as an optimization problem and seek for one or multiple sets of problem parameters which result in close agreement with the available data. A variety of methods can be used to search for an optimal configuration depending on whether sensitivity information (gradient with respect to problem parameters) is available or not. Gradient-based methods use both forward simulations and sensitivity information to locate a local optimal parameter set in the vicinity of the initial guess. When sensitivity information is not available, a variant of direct search techniques which relies on successive forward simulations can be used to navigate the parameter space, a vector space that has the same dimension as the number of problem parameters, and find one or more satisfactory configurations. A common element of known methods is the use of a single likelihood measure to determine whether the predicted data is within an acceptable range of known data. The terms “fitness function” and “objective function” are collectively referred to herein by the term “likelihood measure”. The use of a single likelihood measure provides acceptable performance for many academic or business applications when the likelihood measure is to a great extent convex, smooth, and free of discontinuities.
In practical application, however, geological systems are quite complex and the likelihood measure can be extremely oscillatory with many discontinuities. Known methods of conditioning are practically ineffective for process-based models that model such phenomena based on fundamental physical laws. An improved method of conditioning complex processes-based models such as models of geologic features is desirable.